Reservoir Operation and Flood Management System Framework

Guna Paudyal, M.Eng. Ph.D. Senior Water Resources Management ExpertTeam Leader RTDSS Projects (HP-2)

Drought

Irrigation

Hydropower

Domesticwater

Water quality

Challenges & Technology Requirements

Trans-boundary

Flood

Floods DroughtsOperational Seasonal Strategic

Multiple objectives, stakeholders

Inflow forecastReservoir operation OptimizationFood forecastingWarning disseminationBenefits

Modern IT Based solutions help us…

© DHI

…manage, organise and analyse large amounts

of data

…make wise and robust water management

decisions

…get the full benefit of real-time monitoring and early warning systems

…optimise operations and planning

46 major and medium reservoirsOperated with rigid operational rule curves: keep the reservoirs full towards the end of rainy season.

But when heavy rain occurs in catchments, then the reservoirs are operated releasing sudden floods downstream causing damaging floods.

High Level Government commission: Floods of 2005 and 2006 were devastating, strong needs of Integrated operation of reservoirs were felt. Reservoir operations should consider downstream flooding more explicitly, in addition to other water uses.

Krishna-Bhima basins, 70,000 sq.km)

Ujjani = 3,350 MCMKhadakwasala = 800 MCM

Koyna= 3,000 MCM

Sutlej & Beas Catchments (in India) Decision supports required:

• To attain as high a level as possible in Bhakra and Pong Reservoirs at the end of the monsoon filling period, depending on the acceptable risk of spilling.

• In the event the Reservoir levels exceeds the FRL, to manage spills to minimise downstream flooding.

• to the cushion to leave at the end of the depletion period to meet minimum demands.

• to schedule the flow diverted through the Beas Satluj Link for optimal irrigation and hydropower

Reservoir Operation & Flood Management System Framework

To save livesTo minimize damage

To reduce risk

Data Collection

Transmission& Reception

Emergency Response

Forecasts

Dissemination

Flood Forecasting & Early Warning System

As quickly as

possible

Making information travel faster than flood water

As much time as possible before flood start

Time Delay

Time Delay

Time Delay

Time Delay

NOW!

Future!

Hydrological modelling technology helps to get additional forecast lead time

ProcessInputs Outputs

Precipitation, Evaporation, FlowsReal Time data from RTDAS, met forecasts

Reservoir Details, water demands

Predicted Runoff

Hydrographsfrom all

sub-catchments

Catchment Rainfall-runoff

model

Overview of the Modelling Process

Hydrodynamic River routingFlood Forecast Models

Data AssimilationInundation mapping tools

Data from RTDAS/ Web sites, River & flood Plain topography

Flood Forecast, Early warningFlood maps

Basin Simulation model

Optimal Water Allocation

6 December, 2012© DHI #11

Hydro-met Network (300 telemetry stations)

A Knowledgebase system containing• Historical hydro-met data• Links to RTDAS and Web based data• GIS and other data• Data analysis tools

A suite of models • Catchment hydrology (rainfall-runoff)• Hydro-dynamics• Reservoir operation• Forecasting• Optimization

Interactive Reservoir Operation System

Dissemination system

Flood Bulletin

SMS & E-mail alerts

15

Trial Operation 2013 Monsoon

16

STATISTICAL ANALYSIS OF RESERVOIR WATER LEVEL

6 December, 2012© DHI #17

1-day forecast comparison

2-day forecast comparison

Optimization of Reservoir Operational short term during flood emergencies

Results at Koyna

Reservoir Operational Guidance System (ROS)

Results at Arjunwad (Koyna Complex)

Optimum operation during flood season (Khadakwasala example)

Example of Khadakwasala complex (average year)

Long term operation for optimum water resources management

Optimization of Reservoir Operation(long term operation – planning)

Optimization to satisfy irrigation and water demands

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Optimized WL observed WL

Wat

er L

evel

(m

)

Depleted during dry & average years, filled up in flood years (Pawana)

The BBMB RTDSS Process

Data Acquisition

System

Telemetry Data

IMD Data

RIMES Forecast

Modis Snow Imageries

NASA Satellite Precipitation

Manual Observation Data

Data Storage and

Management

System Architecture

Data Flow

Backup and Security

Modeling Tools

Weighted Rainfall

Rainfall Runoff

Snow Melt

Hydrodynamic

Allocation Model

Flood Models

Results Visualization

and Dissemination

Realtime DSS Interface

Workstations

Remote Locations

Website – Dashboard

Daily Reports

Email and SMS Alerts

Over view of the MIKE Customized RTDSS

Flood Forecasting including inundation d/s of Nangal

Thank you

Specific presentation on the BBMB RTDSS

Details of Krishna - Bhima RTDSS

System Demos: C.S. Modak, Dr. Pandit, Amit Garg, Sagarika

Discussion on Technology: Claus Skotner, DHI Denmark